Push Feeds (e.g., Chainlink Data Feeds) excel at providing high-frequency, low-latency data with robust liveness guarantees. They proactively push aggregated price updates on-chain at regular intervals or when thresholds are breached. This results in exceptional uptime, with major feeds like ETH/USD maintaining >99.9% availability, and predictable, fixed operational costs for dApps. The trade-off is a reliance on continuous off-chain infrastructure and a potential for stale data if the update cadence is too slow for volatile markets.
LABS
Comparisons
Push Feeds vs Pull Reads: Failure Impact
A technical analysis comparing the failure modes, risk profiles, and operational trade-offs between push-based oracle feeds and pull-based oracle reads for blockchain applications.
Chainscore © 2026
introduction
THE ANALYSIS
Introduction: Why Oracle Failure Modes Matter
The architectural choice between push and pull oracles dictates how your protocol fails, making it a critical risk assessment for any CTO.
Pull Reads (e.g., Pyth Network's pull oracle) take a different approach by storing price data with a verifiable cryptographic proof on-chain. Applications pull this data on-demand, paying a minimal gas fee per read. This model eliminates the risk of a feed operator's failure halting your protocol, as the data is already committed. The trade-off is higher per-query latency and variable gas costs for the end-user, which can be prohibitive for high-frequency trading applications but is ideal for less time-sensitive operations like end-of-day settlements.
The key trade-off: If your priority is liveness and predictable cost for high-frequency actions (e.g., a perpetual DEX or money market), choose a Push Feed. If you prioritize censorship resistance, cost efficiency for low-frequency queries, and eliminating single points of failure, choose a Pull Oracle. Your choice fundamentally determines whether your protocol risks stale data or transaction failure.
tldr-summary
Push Feeds vs Pull Reads: Failure Impact
TL;DR: Key Differentiators at a Glance
How each data delivery model handles network stress, downtime, and reliability for critical on-chain applications.
01
Push Feeds (e.g., Chainlink, Pyth)
Proactive, Low-Latency Delivery: Data is pushed to the consumer contract on every update. This ensures sub-second finality for price oracles and events. This matters for high-frequency DeFi (e.g., perpetuals on dYdX, GMX) where stale data means immediate liquidations.
< 400ms
Typical Update Latency
99.9%+
Historical Uptime
02
Push Feeds: The Trade-off
Single Point of Failure Risk: If the feed provider's node or the specific data feed fails, all dependent contracts lose real-time data simultaneously. Requires complex, costly fallback logic (e.g., Circuit Breaker patterns). This is critical for protocols like Aave or Compound where a frozen oracle can halt an entire market.
03
Pull Reads (e.g., Uniswap V3 TWAP, MakerDAO Oracles)
Decentralized & Resilient: Data is not pushed; contracts or keepers pull from multiple sources (e.g., DEX pools) and compute values (like Time-Weighted Average Price). Failure of one source doesn't halt the system. This matters for settlement and collateral valuation where manipulation resistance is paramount.
12-30 sec
Typical Update Window
04
Pull Reads: The Trade-off
Higher Latency & Gas Cost: Data is not real-time. Each update requires an on-chain transaction, incurring gas fees and creating update lags (e.g., 15-minute TWAP windows). This matters for retail trading apps or payment systems where user experience demands instant price confirmation.
PUSH FEEDS VS PULL READS
Head-to-Head: Failure Impact Feature Matrix
Comparison of resilience and operational impact when infrastructure components fail.
| Failure Impact Metric | Push Feeds (e.g., The Graph, Pyth) | Pull Reads (e.g., Direct RPC, Alchemy) |
|---|---|---|
Data Availability During Provider Outage | 0% (Feed stops) | 100% (Historical data accessible) |
Latency Impact on Client | 0 ms (Data is pre-delivered) | 300-2000 ms (Per-request network hop) |
Client-Side Error Handling Complexity | Low (Subscribe/Listen) | High (Retry logic, fallback RPCs) |
Requires Client Infrastructure | ||
Real-Time Data Guarantee | ||
Failure Detection Time | < 1 sec (Heartbeat loss) | On next user request (Variable) |
Typical Cost Model | Per-feed subscription | Per-request (compute units) |
pros-cons-a
Failure Impact Analysis
Push Feed Model: Pros and Cons
Comparing the resilience of proactive data delivery (Push) versus on-demand fetching (Pull) when components fail.
01
Push Feed: Proactive Resilience
Guaranteed delivery to active subscribers: Once an event is emitted (e.g., a new block), the feed pushes it to all listening clients. This ensures downstream systems like indexers (The Graph) or oracles (Chainlink) receive critical data even if they experience temporary processing lag, preventing stale states.
02
Push Feed: Single Point of Failure
Feed provider outage halts all data flow: If the central feed service (e.g., an Alchemy WebSocket, Infura Stream) fails, all connected applications lose real-time updates. This creates a critical dependency, as seen in incidents where provider downtime caused cascading dApp failures across DeFi protocols like Aave and Compound.
03
Pull Read: Decentralized Resilience
No dependency on a single broadcaster: Applications fetch data directly from nodes via RPC calls. If one provider (Alchemy, QuickNode) is down, clients can retry or failover to others. This architecture is favored by wallet providers (MetaMask) and block explorers for its fault tolerance across a multi-provider strategy.
04
Pull Read: Missed Event Risk
Critical latency during high-frequency events: If an application's polling interval is 15 seconds, it can miss transactions or liquidations occurring between cycles. This is a severe risk for high-frequency trading bots or liquidation engines on chains like Solana (>3k TPS), where state changes are rapid and costly to miss.
pros-cons-b
ARCHITECTURAL TRADE-OFFS
Push Feeds vs Pull Reads: Failure Impact
How each data retrieval model handles infrastructure failures, latency, and state consistency. Critical for designing resilient dApps and protocols.
01
Push Feed: Resilience
Decentralized fault tolerance: Data is pushed via a network of indexers (e.g., The Graph, Subsquid). If one node fails, others can serve the query. This matters for mission-critical DeFi protocols requiring 99.9%+ uptime for price oracles and liquidation engines.
99.9%
Uptime SLA
02
Push Feed: Latency
Sub-second finality for subscribers: Events are streamed in real-time via WebSockets (e.g., Alchemy's Notify, Chainlink Data Streams). This matters for high-frequency trading bots and real-time NFT mint tracking where <1s latency is a competitive advantage.
< 1 sec
Event Latency
03
Pull Read: Simplicity & Control
No external dependency risk: DApps directly query RPC nodes (e.g., Alchemy, Infura, private nodes). Failure is isolated to your chosen provider. This matters for enterprise applications with strict SLAs where you control and can failover your own node infrastructure.
1
Failure Point
04
Pull Read: Cost Predictability
No idle resource waste: You pay per request (RPC call) rather than for continuous indexing. This matters for applications with sporadic, user-initiated queries (e.g., wallet balance checks, occasional NFT lookups) where maintaining a full indexer stack is cost-prohibitive.
$0
Idle Cost
05
Push Feed: Complexity & Overhead
Indexer management burden: Requires operating or depending on a decentralized network (The Graph's subgraphs, Ceramic streams). Network lags or syncing issues can cause state divergence. This matters for rapidly iterating protocols where schema changes require indexer re-deployment.
06
Pull Read: Latency & Load
User-experience bottleneck: Every UI action triggers a network call. Under load, public RPC endpoints can throttle requests, causing timeouts. This matters for consumer-facing dApps where slow balance updates or transaction history loads lead to user abandonment.
2-5 sec
Per-Query Latency
FAILURE IMPACT ANALYSIS
Decision Framework: When to Use Which Model
Push Feeds (e.g., Chainlink, Pyth) for DeFi
Verdict: The Standard for Critical Price Feeds. Strengths: High reliability for liquidation engines and perpetuals. A feed failure (e.g., network congestion delaying an update) is a known, bounded risk. Protocols like Aave and Compound use circuit breakers (e.g., pausing markets) as a fail-safe. The model provides deterministic latency for risk calculations. Weaknesses: Centralized points of failure in the oracle network. A prolonged feed halt requires manual intervention.
Pull Reads (e.g., MakerDAO's Oracle Module, Custom RPC Queries) for DeFi
Verdict: Niche for Governance-Critical or Low-Frequency Data. Strengths: Ultimate freshness for governance votes (e.g., MKR voting on collateral parameters) or one-off settlement. Failure is isolated to the specific pull request; other functions remain unaffected. Weaknesses: Unpredictable latency and gas costs during network congestion can break keeper bots. Not suitable for high-frequency trading or liquidation systems.
PUSH FEEDS VS PULL READS
Technical Deep Dive: Failure States and Mitigations
When a blockchain oracle fails, the impact on your dApp depends entirely on its data delivery architecture. This analysis compares the failure modes of push-based feeds (like Chainlink Data Streams) versus traditional pull-based reads (like Chainlink Data Feeds), helping you architect for resilience.
Push feeds fail faster and more visibly. A disruption in a push feed (e.g., Chainlink Data Streams) halts data delivery immediately, causing transactions to revert due to missing updates. In contrast, a silent failure in a pull read (e.g., a stale Chainlink Data Feed) may go undetected until a user transaction reads the outdated value, creating a lag between failure and impact.
Key Mitigation: Push systems require robust off-chain monitoring and fast fallback oracles. Pull systems necessitate on-chain staleness checks and heartbeat monitors.
verdict
THE ANALYSIS
Final Verdict and Recommendation
Choosing between push feeds and pull reads is a fundamental architectural decision that dictates your application's resilience and operational complexity.
Push Feeds (e.g., Chainlink Data Streams, The Graph's Substreams) excel at providing low-latency, deterministic data delivery by proactively pushing updates to subscribers. This model minimizes the risk of missed blocks or stale data for critical on-chain actions. For example, a perpetual DEX like GMX V2 using Chainlink Data Streams can achieve sub-second oracle updates with 99.95%+ uptime, which is non-negotiable for liquidations and price feeds.
Pull Reads (e.g., direct RPC calls, The Graph's historical queries) take a different approach by allowing applications to fetch data on-demand. This results in superior control over data freshness and cost predictability, as you only pay for the queries you execute. However, it introduces points of failure in your client logic and can lead to cascading failures if an RPC provider like Alchemy or Infura experiences an outage during peak load.
The key trade-off is between resilience and control. If your priority is mission-critical, real-time state synchronization (e.g., DeFi lending, on-chain gaming), choose Push Feeds. Their proactive delivery model hardens your system against read-layer failures. If you prioritize cost-effective, flexible querying of non-critical or historical data (e.g., analytics dashboards, NFT metadata), choose Pull Reads. This model offers simplicity and avoids the overhead of maintaining active subscription infrastructure.
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